Machine Learning Resident

"If you are interested in the application of Reinforcement Learning from human feedback methods with generative models and knowledge graphs for RAG based generation, this is the right opportunity for you. Be a part of the team of research and machine learning scientists building from the ground up and get mentored by some of the best minds in AI during the process."

-Mara Cairo, Product Owner, Advanced Technology

Description

About the Role

This is a paid residency that will be undertaken over a 12 month period with the potential to be hired by our client, Vestas, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities. Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly  impactful.

About the Client

With a vision to become the global leader in sustainable energy solutions, everything we do revolves around the development and deployment of sustainable energy solutions. Every day, our employees help to create a better world by designing, manufacturing, installing, developing, and servicing wind energy and hybrid projects all over the world. Our sustainable energy solutions have already prevented a significant amount of CO₂ from being emitted into the atmosphere and contributed to a more sustainable energy system.

We have extensive experience in wind energy and were the first company to reach major landmarks for both the installation and service of wind turbines. As such, we believe we have already played a crucial role in laying the foundations for the sustainable era, and that we are uniquely positioned to show the path to a sustainable planet.

Wind energy is our heritage and core competence. We believe wind will form the backbone of the sustainable energy systems of the future, and we remain focused on developing solutions that accelerate the energy transition and strengthen Vestas' continued leadership in wind.

About the Project

Vestas is facing a growing skills gap in its field service operations. The increasing number of inexperienced technicians, combined with the slow incorporation of field insights into official documentation, has created inefficiencies in turbine troubleshooting and repair. Current documentation is often outdated, difficult to use in real-world conditions, and not tailored to varying technician experience levels. This directly impacts time-to-resolution, first-time-fix rates, and overall technician engagement.

The business challenge is to enable technicians of all skill levels to complete complex troubleshooting tasks accurately and efficiently, while ensuring that expert feedback and new learnings are continuously integrated into updated procedures. The "Virtual Technician" (VT) vision aims to address this challenge by delivering AI-generated, step-by-step repair guides (potentially) enriched with AR validation and a feedback mechanism that closes the loop between technicians in the field and documentation teams.

Required Skills / Expertise

Are you passionate about building great solutions?  You'll be presented with opportunities to both personally and professionally develop as you build your career. We're looking for a talented and enthusiastic individual with a solid background in machine learning, specifically reinforcement learning, ideally with some experience with generative models (e.g., RAG, multi-modal models)

Key Responsibilities:

• Apply advanced reinforcement learning methods (e.g., RLHF, GRPO) with knowledge graphs and generative models to improve guide generation and validation.
• Conduct applied research on reinforcement learning and RAG techniques to overcome limitations in current approaches.
• Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities.
• Collaborate with the project team and stakeholders to develop MVP and client focused solutions.
• Engage in regular client meetings, contributing to presentations and reports on project progress.
• Understanding the Technician/user's cognitive behaviors in order to make the highest possible motivation to engage & provide feedback.

Required Qualifications: 

• Completion of a Computer Science (or a related graduate degree program) MSc. or PhD with specialization in reinforcement learning.
• Theoretical and practical expertise in reinforcement learning, with knowledge of  reinforcement learning from human feedback (RLHF) and multi-armed bandit algorithms.
• Experience with knowledge graphs and generative models (e.g., large language or multi-modal models), including development of retrieval-augmented generation (RAG) systems
• Proficient in developing and training, fine-tuning and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow.
• Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, TensorFlow, PyTorch, OpenCV, Pandas, HuggingFace).
• Solid understanding of classical statistics and its application in model validation.
• Familiarity with Linux, Git version control, and writing clean code.
• A positive attitude towards learning and understanding a new applied domain .
• Must be legally eligible to work in Canada.

Preferred Qualifications:

• Familiarity and hands-on experience with knowledge graphs, as well as text and multi-modal datasets.
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
• Experience/familiarity with software engineering best practices.
• Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.

Non-Technical Requirements: 

• Desire to take ownership of a problem and demonstrate leadership skills.
• Interdisciplinary team player enthusiastic about working together to achieve excellence.
• Capable of critical and independent thought.
• Able to communicate technical concepts clearly and advise on the application of machine intelligence.
• Intellectual curiosity and the desire to learn new things, techniques, and technologies.

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Work under the mentorship of an Amii Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer)
• Build your professional network
• The opportunity for an ongoing machine learning role at the client's organization at the end of the term (at the client's discretion)

About Amii

One of Canada's three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world's top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

How to Apply

If this sounds like the opportunity you've been waiting for, please don't wait for the closing December 05, 2025 to apply - we're excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won't be used in the selection process.